(This article written by Scott Nelson and Bob Rinaldi was posted on the Monitor 101+2025 issue Vol 52 No 5 on September, 3rd 2025)
Scott Nelson and Bob Rinaldi show how data visualization and AI are transforming operations like syndication, revealing hidden trends and driving smarter, faster decisions.
When we were children, we all dreamed of having superhuman powers — the ability to fly, see through walls or lift cars over our heads. But as adults, we dream of a different kind of power; one that doesn’t leap tall buildings but rather deciphers complexity, sees into the future and helps us make better decisions faster.
Not all superpowers come with flashy outfits and capes. In the age of information, one of the greatest human abilities is hiding in plain sight: the power to see patterns, spot anomalies and act fast — all through data. In a world where we’re bombarded by information every second of every day, the ability to make sense of it all has become a competitive advantage. Like a superhero’s sixth sense, data visualization gives us an edge in making smarter decisions in real time and transforming raw numbers into strategic insight, action and success.
DATA VISUALIZATION, A HUMAN SUPERPOWER
Our combination of high-fidelity vision with vast cognitive processing enables both individuals and teams to analyze massive amounts of data quickly, judge available options and make the right decisions for coordinated and collaborative action. And because of the proliferation of screens — from our homes to our cars to the little screen always in our hand — visualization now plays a role in nearly every decision we make and every behavior we practice. But equipment finance business leaders can only leverage their superpower if they have invested in the aggregation and organization of their enterprise data in advanced data repositories, e.g., data marts, data warehouses or data lakes. Only when these state-of-the-art tools are combined with visualization applications and artificial intelligence can leaders uncover less than apparent trends to be used in developing a strategy and vision for the enterprise. Lack of data is kryptonite for today’s business leaders looking for a competitive advantage.
DATA VISUALIZATION, A CATALYST FOR INNOVATION THROUGHOUT HISTORY
From ancient Greece to the digital age, visual innovation has continually expanded human reach. The Greeks enhanced Babylonian maps with coordinates and projections, giving Alexander the Great tools to plan beyond the known world. Centuries later, photography and film transported audiences through time and space, and television brought real-time events into living rooms — creating shared experiences and a visual marketplace that evolved into today’s instant ecommerce with the rise of the internet.
THREE WAYS DATA VISUALIZATION FACILITATES BUSINESS INNOVATION
1. Good data visualization engages teams by communicating key business performance trends while simultaneously facilitating discussion and brainstorming on coordinated actions to improve.
2. Data visualization creates transparency between customers and providers which, when used in real time, enables immediate ideation, trust and action for win-win outcomes.
3. Perhaps most important, good data visualization creates questions: “What if we tried this?”, “Would that work better?” or “I wonder why that happened?” “Why?” is perhaps the most important question today because AI can process vast amounts of data across multiple dimensions to find answers that lead to better behaviors and improved outcomes. Innovation happens only within a culture of questions.
Equipment finance presents fertile ground for visualization innovation due to the complex data flows — from credit risk to portfolio performance — that must be quickly interpreted to guide high-stakes decisions. In a landscape shaped by shifting interest rates, evolving asset values, multi-party transactions and changing borrower behaviors, visual dashboards and real-time analytics bring clarity to vast amounts of data, enabling teams to act with speed, precision and confidence.
RISK-BASED PRICING: PRICING VS. CREDIT SCORE BRINGS CLARITY & INSIGHT FOR TEAMS
The pricing dashboard of ExecutiveAIR, a data visualization toolbox combining data from across the enterprise for executive analysis and action, leverages pattern recognition to provide an immediate understanding of how well a company is pricing for risk. A scatter plot of interest rate versus credit score with selectors for portfolio types by credit tier (A, B, C and D), geography, year and industry segment allows easy investigation into possible sources of the behavior. The consistency of pricing — correlation to a linear trendline — is much tighter for the A portfolio on the right. However, one could also argue that the C portfolio on the left is doing a better job of pricing for risk, as the trendline has a higher slope against the risk scores.
The A-credit team probably has a more disciplined, policy-based pricing, resulting in a tighter fit to the trendline. However, the policy or other parts of the underwriting method appear to engage riskier deals at equivalent costs, which means “money is being left on the table.” The C-credit team appears to be pricing risk more effectively based on the trendline slope. Still, the consistency of the overall underwriting is a challenge given the wide diversity of pricing at any given credit score (Figure 1).
These risk-based-pricing visualizations are very effective in helping leadership teams understand the performance of originations and underwriting. When combined with additional parametric analysis that presents the data by industry, asset types, credit scores, etc., they can see concentrations, stratifications and performance variations within the portfolio. They are sometimes surprised and often say, “I haven’t thought about looking at it that way,” but once they understand the situation, they know they need action. If they don’t know the best action, the visualizations can quickly orient teammates or experts who know the right changes.
Figure 1
CAPITAL MANAGEMENT: TRANSPARENCY & INSIGHT LEAD TO BETTER DECISIONS FASTER
Capital management is the heart of an equipment finance company, but it is often taken for granted by operations, particularly front-end originations. Rarely does a capital management team have its own data analysis tools or visualizations. This can be a big challenge for the business because a mismatch between the credit profiles of the portfolio and the expectations of funders will limit access to more capital.
Transparency through data and ease of communication are two keys for capital management deal makers. “The money has rules” and those rules are met or followed via the alignment of information on syndication deal pools. Simple visualizations of deals (Figure 2) as a function of key parameters of the rules can provide an immediate understanding of whether or not a pool fits a buyer’s expectations. Every seller understands the value of a quick no, and learning from those quick nos can lead to quick yeses as the rules of the buyer are better understood (Figure 2).
(Figure 2), presented by the portfolio manager, allowed the buyer to switch to an Amazon-like mode: “Give me $10 million of that and $5 million of those.” The right data presentation creates a real-time buyer-seller exchange that cannot happen without stratification of the portfolio and visualization of those stratifications. Data stratification enabled rapid analysis and construction of the desired total yield of the syndication, thereby simplifying and accelerating the purchase through a better understanding of the portfolio.
AI SEES EVEN MORE, CREATING QUESTIONS & OPPORTUNITY FOR NEW INNOVATIONS
The introduction of AI to visualization has been a double-edged sword. The long-held axiom ‘seeing is believing’, central to both everyday life and data analysis, is now under duress. Every picture we see on a digital screen can be generated as opposed to being a recording of real life. But AI can also see and identify patterns much more quickly and clearly than a human. AI sees the world with more confidence. Consider the risk-based pricing visuals above. The operations leader sees a mess, a lack of consistency or discipline and lost revenue. But the AI engineer sees a limited understanding of how these decisions are made and how payers pay. Risk cannot be fully characterized with one credit score or few credit factors in a scorecard — there is more to modeling and understanding payment behavior. The distribution of importance of an AI delinquency predictor below shows how the AI evaluates and models the behavior with 38 parameters to create 90% accuracy in its predictions. The many-dimensional nature of risk is part of the “shotgun” pattern in the risk-based pricing scatter charts, and an AI engineer looks to figure out which of the many available data inputs best explain both good underwriting decisions and the distribution of borrower payment behavior. The range of rates for a given credit score is not wrong, but the chart indicates that other, perhaps many other, factors explain the distribution. AI can help provide that explanation and, in the process, provide more precision to identify and correct mistakes (Figure 3).
Similar AI-based learning can happen when predicting the behavior of lenders at the point of origination. AI engineers analyze and model the data to see a lender’s pattern, commonly referred to as the “credit box,” and then build a predictor for those keyholes using the data available at origination. AI prediction enables a sales team to immediately evaluate both the likelihood of finding a funder and, when the capital markets team is put in charge of origination, the alignment of the deal with the enterprise funding strategy. Using AI, the capital markets team can tell the sales team to “go find me deals for this lender because we know that they have an excess of capital that we can access.”
This is where Peter Drucker’s definition of innovation — “a change in behavior that leads to a significant effect on the economy and society” — becomes reality. Data visualization brings people together through understanding and then enables the pursuit of shared objectives. Teams and leadership create innovation faster than individuals. In this context, the visualization of credit data enables an innovative collaboration of origination and capital markets teams to improve the growth engine of the business — and access to capital.
Figure 2: Proper data aggregation and organization enables simple visualizations of pools within the portfolio including evaluation by key credit parameters including time-in-business, credit scores, deal size and deal source to help buyers understand whether the pool fits their requirements.
Data visualization driving innovation is not new, but it is not getting the attention it should today in equipment finance because management is too comfortable with spreadsheets and manual efforts “that work.” Data aggregation, with proper schema development, enables machine learning and AI, making spreadsheet analysis and simple visualizations obsolete. In equipment finance, data visualization converts the phrase “a picture is worth a thousand words” to “a dashboard is worth 20,000 data points and 50 columns in a spreadsheet.”
Figure 3: An AI Delinquency predictor for C-Credit deals uses 38 variables to reach ~90% accuracy.
Data visualization creates innovation by simultaneously presenting and analyzing large amounts of data, communicating both insights and opportunities, and naturally facilitating the coordinated response of teams and partners. Innovation happens when users, specifically employees and customers, change the way they behave to achieve better outcomes. Today, AI is exponentially amplifying the power of visualization because when a human sees patterns that do not make sense, AI can expand the dimensions of analysis as much as needed to build models that explain the situation and accurately predict the action required to achieve success. AI can predict human behavior, and when combined with timely suggestions, AI will also change human behavior, creating even more innovation for those who understand our and AI’s joint superpower — data visualization. •
Scott Nelson is President and CTO of Tamarack Technology.
Bob Rinaldi is President of Rinaldi Advisory Services.